Field
Aspects of the example implementations discussed herein relate generally to data processing and, more particularly, to the summarization, distribution, compression, storage, and visualization of compressed data using a series of dynamically generated cycles.
Related Art
Related art data sources are analyzed and compressed at a networked server device or a client device. Compression is used in the related art to process data and/or transfer the data with fewer bits than the original representation. Typically, compression helps to optimize resources, including transmission, latency, capacity, power, and data storage. However, the design of data compression schemes involves trade-offs among various factors. In lossy data compression schemes, the tradeoff can involve dropping detail from the data source to improve central processing unit (CPU) utilization, improve network transmission, and permit quick comparisons and visualizations. Comparisons may use differential compressions, producing a series of technical artifacts that describe the difference between two data sets. Compression schemes exist for audio and video signal processing and other data streams.
Related art charting tools may visualize a raw data source but they do not overlay or highlight the key points of the compressed data. Related art visualization tools do not separate the noisy data from the essential data.